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Using Subjective Expectations to Forecast Longevity: Do Survey Respondents Know Something We Don’T Know?

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Using Subjective Expectations to Forecast Longevity: Do Survey Respondents Know Something We Don’T Know? Finance and Economics Discussion Series Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington, D.C. Using Subjective Expectations to Forecast Longevity: Do Survey Respondents Know Something We Don’t Know? Maria G. Perozek 2005-68 NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminary materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the research staff or the Board of Governors. References in publications to the Finance and Economics Discussion Series (other than acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers. Using Subjective Expectations to Forecast Longevity: Do Survey Respondents Know Something We Don’t Know? Maria G. Perozek1 14 December 2005 Abstract: Future old-age mortality is notoriously difficult to predict because it requires not only an understanding of the process of senescence, which is influenced by genetic, environmental and behavioral factors, but also a prediction of how these factors will evolve going forward. In this paper, I argue that individuals are uniquely qualified to predict their own mortality based on their own genetic background, as well as environmental and behavioral risk factors that are often known only to the individual. Using expectations data from the 1992 HRS, I construct subjective cohort life tables that are shown to predict the unusual direction of revisions to U.S. life expectancy by gender between 1992 and 2004; that is, the SSA revised up male life expectancy in 2004 and at the same time revised down female life expectancy, narrowing the gender gap in longevity by 25 percent over this period. Further, the subjective expectations of women suggest that female life expectancies produced by the Social Security Actuary might still be on the high side, while the subjective life expectancies for men appear to be roughly in line with the 2004 life tables. 1The author thanks Michael Palumbo, Lise Vesterlund, and Jim Walker for their helpful comments on a previous draft of this paper. The opinions expressed here are those of the author and not necessarily those of the Board of Governors of the Federal Reserve System or its staff. Please address correspondence to Maria Perozek, Federal Reserve Board, mail stop 97, 20th and C Streets NW, Washington DC 20551, USA. Tel: 202 452-2692. Fax: 202 728-5889. Email: [email protected]. 1 Introduction The 20th century witnessed unprecedented improvements in life expectancy: In the United States, life expectancy at birth rose from 47 years in 1900 to 77 years in 2000 (National Center for Health Statistics, 2004).1 Although most demographers agree that mortality rates will continue to decline in the 21st century, there is little consensus on how fast and for how long they will continue to fall (e.g. Vaupel and Lundstrom, 1994; Lee, 2003). The answers to these questions are at the heart of some of the most important issues in the economics of aging, including income adequacy in retirement, and the solvency of the social security system. Many mortality forecasts are based on extrapolations of historical data. However, extrap- olating historical trends may be misleading. For example, simple extrapolative procedures fail to incorporate information about the causes of mortality change over time. This paper provides a somewhat unorthodox alternative to using historical data to project the future path of mortality risk. The method proposed here uses data on individual subjective expec- tations of survival to construct subjective life tables for a particular cohort. This method has an important advantage over extrapolative methods in that subjective expectations in- corporate current and future expected values of variables that influence mortality risk, such as exercise, diet and smoking habits, as well as current and expected advances in medical technology. As much of this information is private, individuals are uniquely qualified to as- sess how these factors will influence their personal mortality risk, which is a function of their medical history, current health status, and family history. By aggregating these individual forecasts of mortality risk across persons in a given cohort, we obtain a subjective cohort life 1Cutler and Meara (2004) provide an excellent overview of the causes underlying mortality improvements over the twentieth century. 1 table that incorporates causal mechanisms implicitly and does not explicitly depend on ad hoc historical trends. The purpose of this paper is to explore the mortality forecasts implied by the subjective expectations of a cohort of individuals in the Health and Retirement Study (HRS) in 1992. There are three main findings: First, the subjective life tables differ significantly from the life tables put together by the Social Security Actuary (SSA) in 1992 and subsequently revised in 2004, and the deviations from the life table differ significantly by gender. In particular, the subjective life expectancies estimated for men are higher than SSA life tables predict, while the subjective life expectancies for women are a good bit lower. Second, these subjective life tables suggest a further narrowing of the gender gap in longevity in coming decades, with men living longer and women dying earlier than is currently predicted by the SSA. Part of this narrowing has already been reflected in revisions to the SSA life tables between 1992 and 2004 in which male life expectancies were revised up and female life expectancies were revised down. In essence, the subjective expectations data from 1992 predicted the direction of revisions to the SSA life tables between 1992 and 2004. The subjective expectations data also suggest that there should be a further narrowing in the gender gap in longevity for these cohorts that is not yet reflected in the SSA life tables. Finally, I demonstrate that the validity of the subjective survivor functions depends crucially on the functional form that governs changes in mortality after age 85. I show that different functional forms result in significantly different life expectancies, largely stemming from the shape of the survivor function beyond age 85; nevertheless I argue that the main findings of the paper are robust to these assumptions. The paper proceeds as follows. Section 2 describes the unique expectations data available in the Health and Retirement Study. The third section demonstrates how these data can be used to construct individual-specific survivor functions, which are then aggregated using 2 population weights for a cohort of men and women in the HRS. The fourth section discusses the resulting subjective life tables and compares their mortality predictions to the life tables produced by the Social Security Actuary in 1992 and then again in 2004. The final section offers concluding remarks and directions for future research. 2 The Health and Retirement Study The data used in this analysis are from the first wave of the Health and Retirement Study (HRS). Initial interviews were conducted in 1992 and provide detailed information on the health status and socio-economic status of a nationally representative sample of persons aged 51 to 61 and their spouses.2 A total of 12,652 individuals were included in the final HRS sample in 1992. Variables of particular importance for this paper include subjective expectations of survival to age 75 and age 85, as well as indicators of the age and sex of the respondent. This paper uses the HRS data on survival expectations to generate sequences of survival probabilities for each individual in the sample. In particular, respondents were asked to answer the following questions: I would like to ask you about the chance that various events will happen in the fu- ture. Using any number from zero to ten, where zero equals absolutely no chance and 10 equals absolutely certain, what do you think are the chances that you will live to be 75 or more? And how about the chances that you will live to be 85 or more? 2Detailed documentation of the HRS is available in Juster and Suzman (1995). 3 When the responses to this question are divided by 10, they can be thought of as probabilities of surviving to age 75 and 85, hereafter referred to as P75 and P85. Hurd and McGarry (1995) suggest that, on average, the subjective survival probabilities are internally consistent: The probability of living to age 75 is greater than or equal to the probability of living to age 85. They also demonstrate that the subjective probabilities are externally consistent in that they covary in reasonable ways with other variables such as health status, and that they are on average in the ballpark of the 1988 life table probabilities. Hurd and McGarry (2002) also demonstrate that subjective survival probabilities have predictive validity; that is, those who survived between waves 1 and 2 of the HRS reported significantly higher probabilities of survival in 1992 than those who died.3 For this analysis, I focus on men and women aged 52 and 57 for comparison to the 1940 and 1935 birth-year cohort life tables, respectively. I drop 2.5 percent of the observations that had reported subjective probabilities that were not internally consistent, i.e., the subjective probability of living to 85 was strictly greater than the subjective probability of living to age 75. Persons who report the same value for P75 and P85 are retained in the sample, but their reported probabilities are altered somewhat in order to estimate the parameters of the survivor functions.4 3More generally, there is an interesting literature on the validity and interpretation of subjective expec- tations data, including Bassett and Lumsdaine (2001), Bernheim (1989, 1990), Dominitz (1998), Dominitz and Manski (1997), Hamermesh (1985), and Manski (1990). 4For practical reasons documented in Appendix A, the subjective expectations data are adjusted for respondents who report P75 = P85, and for respondents who report survival probabilities of zero or one.
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